This script generates panels B and C of supplementary figure 1.

Libraries:
library(Seurat)
library(ggplot2)
library(ggpubr)
library(tidyverse)
library(matchSCore2)
library(here)
Load data:
# Lisi scores
lisi.scores <- readRDS(here::here("results", "lisi.scores", "lisi.scores.RDS"))

# Seurat objects
lymphoid <- readRDS(here::here("data", "adult_aged", "lymphoid.RDS"))
myeloid <- readRDS(here::here("data", "adult_aged", "myeloid.RDS"))
non_immune <- readRDS(here::here("data", "adult_aged", "non_immune.RDS"))
lymphoid_batch_effect <- readRDS(here::here("data", "adult_aged", "lymphoid_batch_effect.RDS"))
myeloid_batch_effect <- readRDS(here::here("data", "adult_aged", "myeloid_batch_effect.RDS"))
non_immune_batch_effect <- readRDS(here::here("data", "adult_aged", "non_immune_batch_effect.RDS"))

# colors
col_lymphoid <- readRDS(here::here("data", "col_lymphoid.RDS"))
col_myeloid <- readRDS(here::here("data", "col_myeloid.RDS"))
col_non_immune <- readRDS(here::here("data", "col_non_immune.RDS"))
col_replicate <- readRDS(here::here("data", "adult_aged", "col_replicate.RDS"))


Supplementary figure 1b

LYMPHOID
batch_corrections_plots <- function(se_obj_batch_effect, se_obj_batch_correction, lisi.score, cols){
  
  gg_features <- list(theme_void(), 
                      theme(legend.text = element_text(size = 14),
                            plot.title = element_text(size = 15, hjust = 0.5),
                            plot.margin = unit(c(0.5,0.5,0.5,0.5), "lines")))
  
  # UMAP no batch effect correction
  umap_batch_effect <- Seurat::DimPlot(se_obj_batch_effect, 
                                       reduction = 'umap', 
                                       cols = cols, 
                                       group.by = "replicate") + 
    labs(title = "no batch effect correction") +
    gg_features
  
  # UMAP after batch effect correction
  umap_batch_correction <- Seurat::DimPlot(se_obj_batch_correction, 
                                           reduction = 'umap', 
                                           cols = cols, 
                                           group.by = "replicate") + 
    labs(title = "after batch effect correction") +
    gg_features
  
  # LISI value
  umap_lisi_score <- lisi.score %>%
    ggplot2::ggplot(aes(UMAP_1, UMAP_2, col = replicate)) +
    geom_point(shape = 16, size = 0.5) +
    scale_color_gradient(low="blue", high="yellow") + 
    labs(title = "LISI value") +
    gg_features


  plots <- list(umap_batch_effect, umap_batch_correction, umap_lisi_score)
  return(plots)
}
LYMPHOID
lymphoid_batch_corrections_plots <- batch_corrections_plots(lymphoid_batch_effect, lymphoid, lisi.scores[["lymphoid_adult_vs_aged"]], col_replicate)

gg <- ggpubr::ggarrange(plotlist = lymphoid_batch_corrections_plots, ncol = 3, nrow = 1)
gg

# save
ggsave(gg, file = here::here("figures", paste0("main_fig_1b.1.pdf")), 
         width = 21, height = 5)
MYELOID
myeloid_batch_corrections_plots <- batch_corrections_plots(myeloid_batch_effect, myeloid, lisi.scores[["myeloid_adult_vs_aged"]], col_replicate)

gg <- ggpubr::ggarrange(plotlist = myeloid_batch_corrections_plots, ncol = 3, nrow = 1)
gg

# save
ggsave(gg, file = here::here("figures", paste0("main_fig_1b.2.pdf")), 
         width = 21, height = 5)
NON-IMMUNE
non_immune_batch_corrections_plots <- batch_corrections_plots(non_immune_batch_effect, non_immune, lisi.scores[["non_immune_adult_vs_aged"]], col_replicate)

gg <- ggpubr::ggarrange(plotlist = non_immune_batch_corrections_plots, ncol = 3, nrow = 1)
gg

# save
ggsave(gg, file = here::here("figures", paste0("main_fig_1b.3.pdf")), 
         width = 21, height = 5)

Supplementary figure 1c

LYMPHOID
matchSCore2::summary_barplot(lymphoid$annotation, lymphoid$replicate) + 
  scale_fill_manual(values = col_lymphoid) +
  labs(title = "CD45+ Lymphoid") +
  theme(plot.title = element_text(size = 15, hjust = 0.5),
        plot.margin = unit(c(0.5,0.5,0.5,2.5), "lines"))

ggsave(file = here::here("figures", paste0("main_fig_1c.1.pdf")), 
         width = 7, height = 5.5)
MYELOID
matchSCore2::summary_barplot(myeloid$annotation, myeloid$replicate) + 
  scale_fill_manual(values = col_myeloid) +
  labs(title = "CD45+ Myeloid") +
  theme(plot.title = element_text(size = 15, hjust = 0.5),
        plot.margin = unit(c(0.5,0.5,0.5,2.5), "lines")) 

ggsave(file = here::here("figures", paste0("main_fig_1c.2.pdf")), 
         width = 7, height = 5.5)
NON-IMMUNE
matchSCore2::summary_barplot(non_immune$annotation, non_immune$replicate) + 
  scale_fill_manual(values = col_non_immune) +
  labs(title = "CD45-") +
  theme(plot.title = element_text(size = 15, hjust = 0.5),
        plot.margin = unit(c(0.5,0.5,0.5,2.5), "lines")) 

ggsave(file = here::here("figures", paste0("main_fig_1c.3.pdf")), 
         width = 6.5, height = 5.5)